library(tidyverse)
## ── Attaching packages ────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
## record_id = col_double(),
## month = col_double(),
## day = col_double(),
## year = col_double(),
## plot_id = col_double(),
## species_id = col_character(),
## sex = col_character(),
## hindfoot_length = col_double(),
## weight = col_double(),
## genus = col_character(),
## species = col_character(),
## taxa = col_character(),
## plot_type = col_character()
## )
ggplot(data = surveys_complete)
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) + geom_point()
library(hexbin)
surveys_plot <- ggplot(data = surveys_complete,
mapping = aes(x = weight, y = hindfoot_length))
surveys_plot +
geom_hex()
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1, color = "blue")
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1, aes(color = species_id))
ggplot(data = surveys_complete,
mapping = aes(x = species_id, y = weight)) +
geom_point(aes(color = plot_type))
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot()
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.3, color = "tomato")
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_violin()
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) +
geom_boxplot(alpha = 0, aes(color = plot_id)) +
geom_jitter(alpha = 0.3)
library(tidyverse)
yearly_counts <- surveys_complete %>%
count(year, genus)
ggplot(data = yearly_counts, aes(x = year, y = n)) +
geom_line()
ggplot(data = yearly_counts, aes(x = year, y = n, group = genus)) +
geom_line()
ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts %>%
ggplot(mapping = aes(x =year, y = n, color = genus)) +
geom_line()
yearly_counts_graph <- surveys_complete %>%
count(year, genus) %>%
ggplot(mapping = aes(x =year, y = n, color = genus)) +
geom_line()
yearly_counts_graph
ggplot(data = yearly_counts, aes(x = year, y = n)) +
geom_line() +
facet_wrap(facets = vars(genus))
yearly_sex_counts <- surveys_complete %>%
count(year, genus, sex)
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(facets = vars(genus))
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(rows = vars(sex), cols = vars(genus))
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(rows = vars(genus))
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(cols = vars(genus))
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
theme_bw()
yearly_weight <- surveys_complete %>%
group_by(year, species_id) %>%
summarize(avg_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = yearly_weight, mapping = aes(x=year, y=avg_weight)) +
geom_line() +
facet_wrap(vars(species_id)) +
theme_bw()
ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw()
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw() +
theme(text=element_text(size = 16))
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
strip.text = element_text(face = "italic"),
text = element_text(size = 16))
grey_theme <- theme(axis.text.x = element_text(colour="grey20", size = 12,
angle = 90, hjust = 0.5,
vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
text=element_text(size = 16))
ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
geom_boxplot() +
grey_theme
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
scale_fill_discrete(name="Sex of Individual",
breaks=c("M", "F"),
labels=c("Male", "Female")) +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
strip.text = element_text(face = "bold", "italic"),
text = element_text(size = 16))
